Developing Crash Modification Factors for Adaptive Signal Control Technologies

Developing Crash Modification Factors for Adaptive Signal Control Technologies
Author :
Publisher :
Total Pages : 44
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ISBN-10 : OCLC:1243905518
ISBN-13 :
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Book Synopsis Developing Crash Modification Factors for Adaptive Signal Control Technologies by : Raul Avelar

Download or read book Developing Crash Modification Factors for Adaptive Signal Control Technologies written by Raul Avelar and published by . This book was released on 2020 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this study was to perform rigorous safety effectiveness evaluations of adaptive signal control technologies (ASCTs) used on urban corridors. To accomplish the goal of this study, the research team compiled safety data from Florida, Texas, and Virginia. Results from Florida and Texas did not offer statistical evidence of a change in safety derived from implementing ASCTs, except for a statistically large and significant reduction in rear end crashes (0.560 crash modification factor (CMF)). Conversely, the results from Virginia produced evidence of significant reductions in total crashes (a 13.3-percent reduction, or 0.867 CMF, at the 10-percent significance level), fatal and severe crashes (a 35.8-percent reduction, or 0.642 CMF, at the 5-percent significance level), and angle crashes (39.6-percent reduction, or 0.604 CMF, at the 5-percent significance level). The research team also conducted an economic evaluation that considered two scenarios: one in which the safety benefit estimated from the Virginia analysis is realized and one in which no measurable safety effect is realized (the worst-case outcome observed in this study), but operational benefits accrue after ASCT installation. When assuming a 13.3-percent reduction in total crashes, the benefit—cost (B/C) ratio was estimated as 65.56. When assuming no safety benefit derived from ASCT installations, the B/C ratio estimate reduced to 25.46. Data were analyzed using multiple estimation methods, including empirical Bayes, full Bayes, and interrupted time series with generalized estimating equations.


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